Lane Model-Constrained Monocular Inertial Visual SLAM for High-Precision Localization in Highway Scenes

Abstract

Continuous stability, as one of the core modules of the autopilot system, is particularly important for its performance. However, as the vehicle speed increases, the system positioning error may be amplified, consequently introducing deviations in the positioning consistency of the system. The inherent high speeds and motion constraints in highway environments introduce new challenges for feature matching, particularly in vision-based vehicle localization, where initialization and scale estimation biases are further expanded. Lane markings, characterized by their simple and uniform structures and high distinctiveness from the surrounding environment, serve as effective features for matching-based localization in autonomous driving. This paper introduces a high-precision and robust vehicle localization method based on lane model constraints. Initially, leveraging lane model parameters from prior maps, we track and model lane line detections across consecutive frames to enhance the completeness of lane representation. The tracking results, combined with prior map data on lane widths, are employed to optimize scale parameters. Subsequently, real-time detected lanes are matched with prior maps through point-map association to constrain the vehicle's heading angle. Finally, map matching results are integrated into existing visual local odometry methods to perform real-time localization optimization, thereby improving localization performance. Experimental evaluations conducted on a self-collected highway dataset demonstrate that the incorporation of lane models significantly enhances system localization accuracy.